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| Content Provider | Springer Nature : BioMed Central |
|---|---|
| Author | Han, Tenghui Zhu, Jun Chen, Xiaoping Chen, Rujie Jiang, Yu Wang, Shuai Xu, Dong Shen, Gang Zheng, Jianyong Xu, Chunsheng |
| Abstract | Background Liver is the most common metastatic site of colorectal cancer (CRC) and liver metastasis (LM) determines subsequent treatment as well as prognosis of patients, especially in T1 patients. T1 CRC patients with LM are recommended to adopt surgery and systematic treatments rather than endoscopic therapy alone. Nevertheless, there is still no effective model to predict the risk of LM in T1 CRC patients. Hence, we aim to construct an accurate predictive model and an easy-to-use tool clinically. Methods We integrated two independent CRC cohorts from Surveillance Epidemiology and End Results database (SEER, training dataset) and Xijing hospital (testing dataset). Artificial intelligence (AI) and machine learning (ML) methods were adopted to establish the predictive model. Results A total of 16,785 and 326 T1 CRC patients from SEER database and Xijing hospital were incorporated respectively into the study. Every single ML model demonstrated great predictive capability, with an area under the curve (AUC) close to 0.95 and a stacking bagging model displaying the best performance (AUC = 0.9631). Expectedly, the stacking model exhibited a favorable discriminative ability and precisely screened out all eight LM cases from 326 T1 patients in the outer validation cohort. In the subgroup analysis, the stacking model also demonstrated a splendid predictive ability for patients with tumor size ranging from one to50mm (AUC = 0.956). Conclusion We successfully established an innovative and convenient AI model for predicting LM in T1 CRC patients, which was further verified in the external dataset. Ultimately, we designed a novel and easy-to-use decision tree, which only incorporated four fundamental parameters and could be successfully applied in clinical practice. |
| Related Links | https://cancerci.biomedcentral.com/counter/pdf/10.1186/s12935-021-02424-7.pdf |
| Ending Page | 15 |
| Page Count | 15 |
| Starting Page | 1 |
| File Format | HTM / HTML |
| ISSN | 14752867 |
| DOI | 10.1186/s12935-021-02424-7 |
| Journal | Cancer Cell International |
| Issue Number | 1 |
| Volume Number | 22 |
| Language | English |
| Publisher | BioMed Central |
| Publisher Date | 2022-01-15 |
| Access Restriction | Open |
| Subject Keyword | Cancer Research Cell Biology Artificial intelligence Machine learning T1 colorectal cancer Real-world research Liver metastasis |
| Content Type | Text |
| Resource Type | Article |
| Subject | Cancer Research Genetics Oncology |
| Journal Impact Factor | 5.3/2023 |
| 5-Year Journal Impact Factor | 5/2023 |
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